LLM-Optimization-Suite: From Generic Models to Business-Grade Intelligence
We don’t just deploy models — we engineer intelligent systems tailored to your workflows, data, and governance needs. From domain-specific tuning to production-ready retraining loops, we deliver LLMs that reason, adapt, and perform in real-world environments.
LLMs don’t break from lack of power — they break when used without context, control, or compliance.
Key Offerings
Domain-Specific LLM Fine-Tuning
We align LLMs to your exact vocabulary, workflows, and knowledge bases, reducing factual drifts and maximizing impact.
- Proprietary dataset fine-tuning: Train models on internal documentation, chats, reports, and more
- Compliance-aware modeling: Fine-tune with privacy-preserving methods for HIPAA, SOC 2, GDPR, etc.
- Contextual grounding: Improve relevance with embeddings, vector search, and domain heuristics.
- Low-latency optimization: Customize model weights for speed, performance, and cost-efficiency.
Domain-Specific LLM Fine-Tuning
We align LLMs to your exact vocabulary, workflows, and knowledge bases, reducing factual drifts and maximizing impact.
- Proprietary dataset fine-tuning: Train models on internal documentation, chats, reports, and more
- Compliance-aware modeling: Fine-tune with privacy-preserving methods for HIPAA, SOC 2, GDPR, etc.
- Contextual grounding: Improve relevance with embeddings, vector search, and domain heuristics.
- Low-latency optimization: Customize model weights for speed, performance, and cost-efficiency.
Retrieval-Augmented Generation (RAG) Pipelines
We build RAG systems that combine model fluency with real-time factual grounding, reducing drift and boosting accuracy.
- Hybrid search architectures: Use dense + sparse retrieval for precision across structured and unstructured data.
- Document-to-response pipelines: Ingest PDFs, manuals, tickets, and wikis into usable response systems.
- Enterprise-grade retrieval logic: Apply permission filters, context rules, and source validation.
- Real-time updates: Maintain up-to-date knowledge without frequent retraining.
Retrieval-Augmented Generation (RAG) Pipelines
We build RAG systems that combine model fluency with real-time factual grounding, reducing drift and boosting accuracy.
- Hybrid search architectures: Use dense + sparse retrieval for precision across structured and unstructured data.
- Document-to-response pipelines: Ingest PDFs, manuals, tickets, and wikis into usable response systems.
- Enterprise-grade retrieval logic: Apply permission filters, context rules, and source validation.
- Real-time updates: Maintain up-to-date knowledge without frequent retraining.
LLM Tool Integration & Orchestration
Move beyond static responses — we integrate LLMs with APIs, databases, and business tools so they can act, not just answer.
- Function-calling + tool use: Let models trigger workflows, schedule tasks, or query systems.
- Dynamic orchestration frameworks: Use LangChain, ReAct, or LangGraph for multi-step planning.
- Intelligent automation powered by LLMs: Connect LLMs to ticketing, CRM, internal APIs, and more.
- Event-driven execution: Build context-aware logic triggered by user events or external systems.
LLM Tool Integration & Orchestration
Move beyond static responses — we integrate LLMs with APIs, databases, and business tools so they can act, not just answer.
- Function-calling + tool use: Let models trigger workflows, schedule tasks, or query systems.
- Dynamic orchestration frameworks: Use LangChain, ReAct, or LangGraph for multi-step planning.
- Intelligent automation powered by LLMs: Connect LLMs to ticketing, CRM, internal APIs, and more.
- Event-driven execution: Build context-aware logic triggered by user events or external systems.
Model Evaluation, Guardrails & Testing
We don’t just fine-tune. We rigorously evaluate outputs for quality, bias, latency, and alignment with business KPIs.
- Continuous evaluation pipelines: Measure relevance, accuracy, and coherence at scale.
- Bias + drift detection: Catch failure modes and hallucinations before they reach production.
- Guardrails & moderation layers: Apply filters, escalation rules, and security triggers.
- Regression testing: Ensure updates don’t break workflows or compliance logic.
Model Evaluation, Guardrails & Testing
We don’t just fine-tune. We rigorously evaluate outputs for quality, bias, latency, and alignment with business KPIs.
- Continuous evaluation pipelines: Measure relevance, accuracy, and coherence at scale.
- Bias + drift detection: Catch failure modes and hallucinations before they reach production.
- Guardrails & moderation layers: Apply filters, escalation rules, and security triggers.
- Regression testing: Ensure updates don’t break workflows or compliance logic.
Post-Deployment Feedback Loops
Once deployed, your models will learn from real-world usage and evolve with changing business needs.
- Auto-labeling + feedback ingestion: Train on implicit and explicit signals from users.
- LLMops pipelines: Automate retraining, deployment, and version control.
- KPI-aware tuning: Optimize for outcomes like resolution rate, CSAT, or lead conversion.
- LLM telemetry dashboards: Monitor what the model is saying, doing, and improving.
Post-Deployment Feedback Loops
Once deployed, your models will learn from real-world usage and evolve with changing business needs.
- Auto-labeling + feedback ingestion: Train on implicit and explicit signals from users.
- LLMops pipelines: Automate retraining, deployment, and version control.
- KPI-aware tuning: Optimize for outcomes like resolution rate, CSAT, or lead conversion.
- LLM telemetry dashboards: Monitor what the model is saying, doing, and improving.
What Clients Say About Altpai
“Altpai helped us layer agentic workflows over legacy systems without rewriting a single core module. We got automation, orchestration, and observability without disruption. Their team understood both the tech and the enterprise risk. More than a deployment, it was a precision retrofit built to scale with our operational architecture.”
“Altpai engineered our AI infrastructure to be fully cloud-agnostic, with telemetry, RBAC, and security protocols embedded from day zero. Their approach was foundational. We now have an AI-native stack that meets compliance, scales predictably, and integrates seamlessly across Azure, AWS, and our sovereign deployment zones.”
“Our initial LLM plateaued at 40% accuracy. Altpai overhauled it with RAG, embeddings, and a feedback loop for retraining. Within weeks, the system was self-improving in production, accurate, relevant, and context-aware. Now, it’s a living system embedded in our decision workflows.”
“Altpai deployed a fraud detection engine that detects and evolves. With agentic logic and daily retraining, our false positives dropped dramatically. What impressed me most was the zero-lag adaptation. This isn’t just AI but operational intelligence that learns on the job.”
“Our PoC was production-ready in just four weeks. By week eight, Altpai had built a retrainable MVP, fully instrumented and aligned to our GTM strategy. No bloated dev cycles. No handovers. Just velocity, quality, and the infrastructure to scale. They didn’t consult; they executed.”